




import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase							:						str					       =  logging.get_logger(__name__)


lowerCamelCase							:						List[str]					       =  {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCamelCase							:						Tuple					       =  {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowerCamelCase							:						str					       =  {"facebook/blenderbot_small-90M": 512}

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict	):


      '''simple docstring'''



      lowerCamelCase_							=       set()
      lowerCamelCase_							=       word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)	)
            lowerCamelCase_							=       char

      lowerCamelCase_							=       set(lowercase	)
      return pairs







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		VOCAB_FILES_NAMES
 UpperCamelCase		  =		PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase		  =		['''input_ids''', '''attention_mask''']

 def __init__(							self   :    Tuple		,				A_   :    Dict		,				A_   :    List[Any]		,				A_   :    List[Any]="__start__"		,				A_   :    Any="__end__"		,				A_   :    Tuple="__unk__"		,				A_   :    str="__null__"		,				**A_   :    Optional[Any]		,				)  ->      Optional[Any]:

       """simple docstring"""






       super().__init__(unk_token=A_		,				bos_token=A_		,				eos_token=A_		,				pad_token=A_		,				**A_    )

       with open(A_		,				encoding='utf-8'    ) as vocab_handle:
             lowerCamelCase_							=       json.load(A_    )
       lowerCamelCase_							=       {v: k for k, v in self.encoder.items()}
       with open(A_		,				encoding='utf-8'    ) as merges_handle:
             lowerCamelCase_							=       merges_handle.read().split('\n'    )[1:-1]
       lowerCamelCase_							=       [tuple(merge.split()    ) for merge in merges]
       lowerCamelCase_							=       dict(zip(A_		,				range(len(A_    )    )    )    )
       lowerCamelCase_							=       {}

 @property
 def 							a__					(							self   :    str    )  ->      int:

       """simple docstring"""






       return len(self.encoder    )

 def 							a__					(							self   :    Optional[Any]    )  ->      Dict:

       """simple docstring"""






       return dict(self.encoder		,				**self.added_tokens_encoder    )

 def 							a__					(							self   :    str		,				A_   :    str    )  ->      str:

       """simple docstring"""






       if token in self.cache:
             return self.cache[token]
       lowerCamelCase_							=       re.sub('([.,!?()])'		,				r' \1'		,				A_    )
       lowerCamelCase_							=       re.sub('(\')'		,				r' \1 '		,				A_    )
       lowerCamelCase_							=       re.sub(r'\s{2,}'		,				' '		,				A_    )
       if "\n" in token:
             lowerCamelCase_							=       token.replace('\n'		,				' __newln__'    )

       lowerCamelCase_							=       token.split(' '    )
       lowerCamelCase_							=       []
       for token in tokens:
             if not len(A_    ):
                   continue

             lowerCamelCase_							=       token.lower()
             lowerCamelCase_							=       tuple(A_    )
             lowerCamelCase_							=       tuple(list(word[:-1]    ) + [word[-1] + '</w>']    )
             lowerCamelCase_							=       get_pairs(A_    )

             if not pairs:
                   words.append(A_    )
                   continue

             while True:
                   lowerCamelCase_							=       min(A_		,				key=lambda A_    : self.bpe_ranks.get(A_		,				float('inf'    )    )    )
                   if bigram not in self.bpe_ranks:
                         break
                   lowerCamelCase_	,							lowerCamelCase_							=       bigram
                   lowerCamelCase_							=       []
                   lowerCamelCase_							=       0

                   while i < len(A_    ):
                         try:
                               lowerCamelCase_							=       word.index(A_		,				A_    )
                               new_word.extend(word[i:j]    )
                               lowerCamelCase_							=       j
                         except ValueError:
                               new_word.extend(word[i:]    )
                               break

                         if word[i] == first and i < len(A_    ) - 1 and word[i + 1] == second:
                               new_word.append(first + second    )
                               i += 2
                         else:
                               new_word.append(word[i]    )
                               i += 1
                   lowerCamelCase_							=       tuple(A_    )
                   lowerCamelCase_							=       new_word
                   if len(A_    ) == 1:
                         break
                   else:
                         lowerCamelCase_							=       get_pairs(A_    )
             lowerCamelCase_							=       '@@ '.join(A_    )
             lowerCamelCase_							=       word[:-4]

             lowerCamelCase_							=       word
             words.append(A_    )
       return " ".join(A_    )

 def 							a__					(							self   :    Tuple		,				A_   :    str    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       []

       lowerCamelCase_							=       re.findall(r'\S+\n?'		,				A_    )

       for token in words:
             split_tokens.extend(list(self.bpe(A_    ).split(' '    )    )    )
       return split_tokens

 def 							a__					(							self   :    Tuple		,				A_   :    str    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       token.lower()
       return self.encoder.get(A_		,				self.encoder.get(self.unk_token    )    )

 def 							a__					(							self   :    Tuple		,				A_   :    int    )  ->      str:

       """simple docstring"""






       return self.decoder.get(A_		,				self.unk_token    )

 def 							a__					(							self   :    Optional[Any]		,				A_   :    List[str]    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       ' '.join(A_    ).replace('@@ '		,				''    ).strip()
       return out_string





 def 							a__					(							self   :    Tuple		,				A_   :    str		,				A_   :    Optional[str] = None    )  ->      Tuple[str]:

       """simple docstring"""






       if not os.path.isdir(A_    ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
             return
       lowerCamelCase_							=       os.path.join(
           A_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
       lowerCamelCase_							=       os.path.join(
           A_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

       with open(A_		,				'w'		,				encoding='utf-8'    ) as f:
             f.write(json.dumps(self.encoder		,				indent=2		,				sort_keys=A_		,				ensure_ascii=A_    ) + '\n'    )

       lowerCamelCase_							=       0
       with open(A_		,				'w'		,				encoding='utf-8'    ) as writer:
             writer.write('#version: 0.2\n'    )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,				key=lambda A_    : kv[1]    ):
                   if index != token_index:
                         logger.warning(
                             f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                             ' Please check that the tokenizer is not corrupted!'    )
                         lowerCamelCase_							=       token_index
                   writer.write(' '.join(A_    ) + '\n'    )
                   index += 1

       return vocab_file, merge_file







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''laion/clap-htsat-unfused'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return RobertaTokenizer.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							ClapProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ClapProcessor(tokenizer=self.get_tokenizer()     ,   feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''       )
         snake_case											=							self.get_feature_extractor(do_normalize=__snake_case     ,   padding_value=1.0       )

         snake_case											=							ClapProcessor.from_pretrained(
             self.tmpdirname     ,   bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''     ,   do_normalize=__snake_case     ,   padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							floats_list((3, 1_0_0_0)       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audios=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							'''This is a test string'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names[2:]     ,   feature_extractor.model_input_names     ,   msg='''`processor` and `feature_extractor` model input names do not match'''     ,   )




